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They are widely used in narrow circles :)
Two of (arguably the best) open source RC aircraft flight controllers (ArduPilot and PX4) are using extended Kalman filters in their state estimators (essentially sensor fusion that provides attitude/position estimate):
https://github.com/ArduPilot/ardupilot/tree/master/libraries...
https://github.com/PX4/PX4-Autopilot/blob/main/src/modules/e...
I'm not that familiar with cleanflight/betaflight/inav scene to know what the FPV racer flight controllers use.
They are widely used in narrow circles :)
Two of (arguably the best) open source RC aircraft flight controllers (ArduPilot and PX4) are using extended Kalman filters in their state estimators (essentially sensor fusion that provides attitude/position estimate):
https://github.com/ArduPilot/ardupilot/tree/master/libraries...
https://github.com/PX4/PX4-Autopilot/blob/main/src/modules/e...
I'm not that familiar with cleanflight/betaflight/inav scene to know what the FPV racer flight controllers use.
That paper [1] is from 2010. What did "industry" use before that for pysically moving objects?
If this is the current state of the art, are there generally-available/open-source libraries existing that implement this and practitioners use for this?
The only one I could find is https://github.com/kartikmohta/manifold_cdkf, which currently has 8 Github stars.
I also found an approach mentioned in [2] that is to just treat a single rotation angle as linear, and then wrap it around at 180 degrees in between state updates with additional conditional logic. Is this what people did in practice before? I cannot find substantial info on this.
How did people use KF on physical objects before 2010?
[2]: https://old.reddit.com/r/ControlTheory/comments/d2yrjq/kalma...